Artificial intelligence (AI) is no longer “on the rise” or simply a passing trend — this disruptive technology is here to stay. In our 2023 AI research report, a third of people said they’d used generative AI tools. Of those folks, 55% said they’d use them again. 

According to research from McKinsey, the impact of generative AI on productivity could potentially add trillions of dollars in value to the global economy. As e-commerce leaders, there’s multiple ways it can work for us — to save time and resources and ultimately help our businesses grow. 

Chapters:

1. Three key benefits of AI marketing for e-commerce
2. How AI marketing works
3. Top applications of AI marketing
4. Drive your marketing forward with AI

Three key benefits of AI marketing for e-commerce

AI helps create smoother and more efficient internal operations that extend to the customer during the buying process. As Benj Fein, Group Project Manager at Bazaarvoice, puts it, “AI lowers the cost of your workflows so you can work faster and smarter.”

Here’s the top three key benefits. 

1. Improved marketing efficiency

AI empowers marketing teams to automate tasks so they can work smarter and achieve more significant results. Using tools with these automated capabilities takes the more time-consuming tasks off your plate, so you have time to focus on creating successful marketing campaigns and meeting company goals. 

AI lowers the cost of your workflows so you can work faster and smarter

It’s not just simple tasks that can be automated, either. E-commerce companies can employ AI to automate a lot of different marketing functions, including email campaigns, social media posts, ad targeting, and content optimization.

AI-driven tools analyze customer data and behavior to deliver personalized and timely content, streamlining processes and increasing efficiency.

2. Faster customer feedback and CX insights

When it comes to customer feedback analysis, AI achieves quicker and more detailed insights than traditional methods. Using natural language processing (NLP), AI can decipher customer experience and sentiment signals, like keywords linked to touchpoints and activities, and emotions that correlate with complaints or positive experiences.

Other tools like customer satisfaction surveys and Net Promoter Scores can’t extract this kind of nuanced data.

The CX insights that AI can unearth have a range of business benefits for marketing professionals. AI can reveal misalignment in customer pain points, expectations, and unmet needs. This information can help treat CX issues in real-time, as well as inform ongoing processes, procedures, and strategies.

For example, AI can analyze customer behavior, purchase history, and preferences which is information you can use to offer personalized recommendations. 

A better understanding of CX can improve staff training to provide better shopping experiences. This, in turn, leads to customer retention, loyalty, and long-term growth.

3. Personalized marketing messages

The use of AI in crafting targeted marketing messages has become increasingly common, offering e-commerce companies a powerful tool to streamline their communication strategies. AI uses keywords and sentiments to create highly personalized content that aligns with customer preferences.

Using the feedback and insights generated by AI, e-commerce businesses can now craft marketing messages that foster more meaningful connections with individual customers. 

A common example of this is automated A/B testing. AI can efficiently evaluate different message variations and identify the most effective ones based on real-time customer responses and engagement metrics. This iterative approach allows marketers to continuously refine their messaging strategies and deliver content that maximizes customer engagement and conversion rates.

How AI marketing works

AI e-commerce marketing uses data to assess customer behavior and intent through machine learning. AI tools can then take action by interacting with customers or providing predictions and recommendations for the appropriate business departments.

Because AI relies on data, e-commerce companies need to have data resources in place, including customer relationship management (CRM) software, campaign insights, and website data. They also need to establish goals for using AI and have internal or outsourced experts train on the technology. That way, you get the desired outcomes based on those goals. Part of AI’s capability is to automatically improve its performance with experience and management.

The companies experiencing the best results from AI are the ones that use advanced data, technology, and models in addition to core best practices. So, having an internal or third-party team dedicated to AI and data development will yield the best ROI.

Applications of AI marketing

Deloitte estimates the generative AI market will likely double every other year for the next ten years. You can capitalize on this growing trend by adopting AI for some of your marketing functions. Here’s a few common applications of AI marketing. 

Conducting keyword research

E-commerce companies can use AI to supercharge their keyword research efforts, gaining valuable insights into customer behavior and preferences.

AI augments traditional keyword research by leveraging its capabilities in NLP and data analysis. When you employ an AI-driven tool like Jasper or Surfer SEO, you can quickly process large amounts of data from search engines, websites, and social media platforms to identify relevant keywords and uncover hidden patterns in customer search behavior.

Using automation, you can uncover the words and phrases that resonate most with the target audience, enabling you to tailor your content and product offerings accordingly. This level of personalization can lead to better customer experiences and higher satisfaction. According to McKinsey, 71% of people expect brands to personalize their interactions, and 76% of people get frustrated when interactions aren’t personalized. 

AI’s ability to analyze vast data sets quickly and accurately allows for real-time keyword monitoring. This means that e-commerce companies can stay on top of emerging trends and adapt their keyword strategies promptly, maintaining a competitive edge in the fast-paced online market. 

You can incorporate these AI-generated keywords into your website copy, social media discussions, product descriptions, advertising campaigns, and more. By adding these keywords to your website copy, more potential customers will find you online.

And because the AI did the heavy lifting with the keyword research, your team has the time to engage directly with those potential customers.

Implementing voice commerce

Voice commerce refers to the ability for consumers to use voice commands for product search, discovery, and even purchasing. Globally, over four billion digital voice assistants are in use, including Apple’s Siri and Amazon’s Alexa. It’s a quick, convenient way for people to take an action — like shopping.

Voice commerce eliminates the need for manual input, which streamlines the purchasing process significantly. Users can simply say a command and the AI-powered voice assistant quickly responds with relevant product options and information. This level of convenience and speed saves valuable time for consumers, allowing them to complete purchases effortlessly, no matter where they are or what they’re doing.

Enable these digital voice assistants for your e-commerce app and website so customers can use them to browse and buy. You can register your business with Siri, Alexa, or other digital voice assistants, so customers can easily find your brand and products through voice commands on their devices. 

Creating effective advertising copy 

Use AI to craft compelling advertising messages that engage your target audience effectively. AI tools that use NLP can assess the language and tone that resonate best with your target audience. You can use these tools to make sure your ad copy aligns with your brand’s voice while appealing to their emotions and preferences. Through advanced algorithms, AI can craft unique copy that aligns with specific customer interests, boosting engagement and conversion rates.

By leveraging AI-driven data analysis, e-commerce companies gain a deeper understanding of their audience’s preferences and pain points. From there, you can use prompts with AI tools like ChatGPT to create copy that directly addresses customer needs, leading to higher relevance and resonance.

And it’s incredibly easy to do once you learn the program! You simply input customer data, such as preferences, purchase history, and social media interactions, into your AI copywriting tool. From there, the AI tool analyzes this data and generates personalized ad copy for each customer segment, highlighting relevant products and appealing to their unique style preferences.

AI tools can integrate with your existing marketing platforms, such as social media, email marketing, and website content management systems. This integration ensures a seamless data flow between your AI tool and all your other company tools.

You should also segment your audiences based on key characteristics so your AI tool can create more personalized copy for each audience. 

Improving product discovery

AI analyzes data to deliver personalized marketing messaging, especially in the shopping discovery stage. A big part of that is product recommendations on e-commerce sites, apps, and social selling channels. AI uses customers’ purchase history and other shopping behaviors, like product page views and abandoned cart items, to showcase relevant product recommendations. Personalized product recommendations are particularly helpful for those brands with extensive product catalogs.

There’s a lot of examples of how AI strengthened product discovery for large, prominent brands. IKEA leveraged Google Cloud’s Recommendations AI to create a better search experience for their customers with personalized product recommendations. Implementing this technology delivered accurate recommendations to customers in a quick, user-friendly fashion. The result was a 400% increase in relevant product recommendations, a 30% increase in click-through rate, and a 2% increase in average order value.

Better search means a better overall e-commerce experience, which the majority of consumers and website managers say leads to a high likelihood of repeat business. Not delivering relevant, helpful content to customers has consequences. Customers who can’t find one item they’re looking for will abandon their entire cart, and three out of four will give up and go to a competitor after an unsuccessful search.

Staples Canada significantly enhanced their product discovery performance with AI. With more advanced search results and personalized product recommendations, they’re able to show many more customers the products they’re looking for that also match their interests. This implementation led to a significant increase in conversion rate.

Likewise, Wayfair uses a multifaceted AI system to produce precise yet thorough search results on its e-commerce site. They train their algorithm to detect visual elements, like design features, color, material, style, and more, from customer, supplier, and rendered photos. They also manually tag their products with relevant keywords so the algorithm produces broader search results based on keywords and visual elements.

This feature led to an increased add-to-cart rate and completed purchases.

Presenting product reviews and detecting fake reviews

It’s well understood in e-commerce that product reviews are an effective tool for converting shoppers into buyers. AI can manage, organize, and display your product reviews from customers, which is essential for large-scale businesses with lots of products and reviews to wade through.

“In the purchasing stage, AI can help showcase reviews that can convince consumers to make the purchase,” says Fein. For example, Bazaarvoice’s reviews tools have a range of features powered by AI, like highlighting the most helpful reviews from a large volume of reviews and displaying them strategically throughout your e-commerce site.

AI can also scan your entire product catalog and identify items gaining traction that have few to no reviews. Fein says this automated process enables you to identify which products need review coverage or other content that adds value for customers, like visual user-generated content (UGC).

Insulated water bottle brand Takeya leveraged Bazaarvoice tools supported by AI to increase sales on their own site and their retail partner site, Target.com. They were able to display reviews for all of their products on both sites and automatically add visual UGC sourced from social media to corresponding products.

Detecting fake reviews

Ironically enough, AI can also help you detect fake reviews from bots that use AI. This allows you to remove them and protect your brand image and reputation. Consumers are much more discerning about reviews than they were several years ago — they can easily pinpoint a fake review from an authentic one. 

Using NLP, AI tools can comb through reviews on your website, social, and Google to examine the language and tone used in the text. Fake reviews often exhibit certain patterns, such as an excessively positive tone with repetitive phrases.

AI can also detect suspicious language patterns and grammar inconsistencies commonly found in fake reviews. It can compare the writing style of other reviews to look for indications of automation or bots. 

Enhancing the customer experience

A primary function of AI for marketing is to support CX teams. With the help of AI, they can handle customer interactions on a much larger scale and gain insights to refine their processes.

Chatbots

Chatbots are a classic example of AI in action. When interacting with customers, chatbots can provide personalized responses that go beyond the typical FAQ answer. A survey of high-level operations and CX professionals concluded that companies that invest in advanced conversational AI tech will reap benefits, including decreased operational costs, more productive agents, and more satisfied customers.

Conversational AI is instrumental in capturing and guiding customers during the research and discovery stage. It can quickly answer questions with live chat, enable purchasing via a chatbot, use NLP to correspond with users, and offer hands-free voice assistance. AI bots can also respond to user actions with prompts designed to inspire purchases, like validating their cart item by announcing that another customer just bought the same thing. This kind of messaging can increase conversion rates by 5x or more.

Some types of conversational or chatbot AI can also offer guidance to the human reps on the other end of the screen. They can analyze the customers’ tone and recommend different responses for the best resolution or suggest that a supervisor step in to manage the inquiry or issue.

Airlines are examples of companies that get a lot of customer service activity. And for a rapidly growing airline, that can be overwhelming. Before adopting an AI approach to customer service management, AirAsia relied on localized call centers to handle inquiries from international locations, which meant up to hour-long wait times and time zone conflicts.

To resolve this problem, AirAsia implemented a 24-hour, multilingual chatbot that could check flights, book flights, answer questions, update passenger information, and add bonus products to bookings. The ROI included a 98% wait time improvement, an 8x increase in upsells, and significant live agent relief.

Translation technology

With machine translation, NLP, and other capabilities, AI can automatically take your marketing efforts global. This is more than just translating basic small pieces of web content in other languages, which often doesn’t produce the most accurate or natural results. AI can pick up on tone and context to provide quality translation and communication on a larger scale. As with other AI applications, this doesn’t replace professional human staff but makes their job easier and more efficient.

The translation technology industry is in high demand and thriving because it can provide significant support in translating documents, software, and websites in multiple other languages. This is crucial for meeting brand, communication, and legal standards for all pertinent audiences.

Analyzing customer feedback

AI can mine through all your customer touchpoints — from website comments and messages to product reviews and social media interactions — to synthesize all kinds of customer feedback. These insights keep you up to date on any service or product issues that need resolving and let you know what customers are liking. So you can give them more of it. AI tools can organize this info by customer, making robust customer profiles by tracking positive or negative experiences, questions, product reviews, and more.

AI algorithms can understand vocabulary that expresses a range of emotions and perspectives that “can directly shape both short-term and long-term actions to retain customers,” according to Harvard Business Review. And it can deliver this analysis in real-time, so staff across departments can all be aligned on customer behavior and pain points.

Customers aren’t shy when it comes to making their grievances or exceptional experiences heard. That’s why Bazaarvoice’s customer sentiment analysis tools give you the full rundown of what customers are saying using advanced machine learning and NLP. Not only that, but our Premium Network Insights can categorize sentiment by topic and product and compare that feedback against your competitors.

Boosting efficiency and strategy optimization

AI supplies you with insights and goes the extra mile to recommend what actions to take in response. Fein considers this one of AI’s best functions. “The most effective features in AI for marketers are ones that are built with data personal to marketers, [so] the system is saving time making recommendations similar to what the marketer would choose if they had more time themselves,” he says.

An example of this is using customer reviews as a resource to inspire product and messaging improvements. The toy manufacturer KidKraft uses Bazaarvoice Insights tools to evolve their brand according to customer demand. These tools provide KidKraft with, “an automated way to understand what customers are saying about its products and see recommended actions the brand can take to improve its marketing strategy and design better products.”

This extends to social content as well. Fein says part of Bazaarvoice’s AI can, “highlight trends in your social strategy so that the content you are spending time pushing to your discovery path [is] more likely to receive engagement.” That capability includes recommending the types and timing of content that are likely to perform well based on your past content.

AI can even predict the success of partnering with different influencers. “We have AI to help estimate the value of content generated by your influencers to help you gauge the ROI of those relationships,” says Fein.

AI tools can scan product catalogs to reveal product pages that require content coverage like reviews and visual UGC to display that would enhance the product’s presentation. Customers who interact with this type of UGC on e-commerce sites are twice as likely to convert.

“AI can benefit e-commerce brands by [reducing] the amount of steps in scaling their business. When managing a full catalog and ensuring it’s properly covered in the tons of channels out there, AI can help step in and help automate some of the steps in that process,” says Fein.

Drive your marketing strategy forward with AI

Don’t you wish you could go back in time and invest in Tesla? Back when everyone else was still scoffing at the idea of a luxury electric car? Well, that’s the current situation with AI, and specifically AI marketing. Keep an eye on AI developments and grow with the technology as it evolves and expands.

And remember: AI doesn’t replace your marketing department. It complements and enhances it. here’s some examples of how top brands are using AI, for your inspiration

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Masterclass: How to use AI strategically and responsibly

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This guide explains what customer sentiment analysis (opinion mining) is, types of customer feedback and sentiment to focus on, and what actions to take based on insights to improve products and customer experience.

Chapters:

1. What is sentiment analysis?
2. Types of sentiment analysis you can perform
3. Four steps of the sentiment analysis process
4. How to respond to types of customer sentiment
5. 3 brand examples of sentiment analysis success
6. Drive meaningful change with insights and reports

Imagine asking your roommate if they’d mind taking out the trash three times in one day. You didn’t say, “I’m frustrated,” but the fact that you’ve made this trash request multiple times within 24 hours is a pretty clear sign that you’re peeved.

That situation is sentiment analysis in a nutshell. Brands review the language that customers use to guess how they feel about your company. It’s a powerful tool for uncovering and addressing customer issues, boosting brand loyalty, and winning over future customers. 

What is sentiment analysis?

Sentiment analysis, or opinion mining, uses data to measure and understand customer sentiment and opinions about a brand and its products. To conduct this analysis, brands use tools that review text with natural language processing (NLP) algorithms.

The technology identifies negative and positive words in customer communication and assigns an overall sentiment score. 

The goal of sentiment analysis is to understand how customers perceive your brand and use that information to improve your customer experience. If you find out through analyzing the text in your online reviews that customers love a new lip gloss you just added, you might create others with similar colors and scents. You can also use sentiment analysis models to

• Help with market research
• Find new use cases for your products
• Get a feel for your brand reputation

Keep in mind, sentiment analysis does come with some challenges. It’s difficult for NLP tools to detect sarcasm and interpret slang words in a message. A sentiment analysis tool might identify a backhanded compliment as a positive message. Multilingual messages may also be a challenge for an NLP tool, which is typically trained in one primary language and may not pick up on some of the nuances if a message is translated. 

Types of sentiment analysis you can perform

There’s several models of sentiment analysis, and each one has different purposes and provides different insights into your customers’ feelings. All sentiment analysis methods are used to understand your customers’ mindset, but where some involve using rating systems, others predict intent.

Fine-grained sentiment analysis

Fine-grained sentiment analysis is the process of categorizing customer feedback on a scale ranging from very positive to very negative.

Say you send a customer satisfaction survey with answers on a scale from 1 to 5. Fine-grained analysis would involve collecting those responses and sorting their feedback.

Or if you were to give customers open-ended questions, the sentiment analysis tool will also break down their sentences to look for any positive or negative words. It categorizes those responses for you so you can sort through them and find common threads.

So, suppose multiple customers rate the quality of a certain brand of shoe as negative. In that case, you know your company needs to look into quality assurance processes or suppliers and adjust your shoe-making process.

Emotion detection

Emotion detection is a process of analyzing customer language to identify their feelings. Are they angry or joyful about a product or the business? The results from an emotion detection analysis aren’t just restricted to positive or negative — emotion categories include anger, surprise, joy, disgust, anxiety, desire, and sadness. Or the less popular Seven Dwarves.

For emotion detection, you’ll need to add lexicons with lists of words that indicate positive or negative feelings to your NLP tool, like “frustrated,” “unhappy,” or “thrilled.” Once the sentiment analysis tool has those words in place, it will comb through your reviews, live chat transcripts, social media comments, and other text communications to sort them into emotion categories. 

For example, your social media manager might report an increase in negative comments on Facebook, Instagram, and Twitter. You’d then run an emotion detection analysis on those to pinpoint the exact emotions your audience is feeling. Maybe they’re angry about a product you removed from your store, or anxious about new billing processes.

Once you know what’s contributing to their feelings, you can create an action plan to address the issues.

Aspect-based sentiment analysis

In aspect-based opinion mining, the purpose is very specific — to find trends in customer comments about traits of your products. You’ll then use that information to make changes in your products or services if they’re needed. 

Let’s say you sell hockey bags and recently switched zipper suppliers, and want to know how customers feel about the quality of new zippers. Your aspect-based sentiment analysis might reveal that the zipper broke or stuck for the majority of customers, indicating you need to make an adjustment. 

Or, if you recently changed an option in your subscription box, perform one of these analyses to see how customers’ feelings changed after the upgrade. If they’re using words like “frustrated” or “annoyed,” it’s safe to say they’re not loving the new change. 

Intent-based sentiment analysis

An intent-based sentiment analysis attempts to predict your customer’s interests and intentions when they’re interacting with your company. 

If a customer messages and asks, “Is there a discount on this item?” the NLP software might assume they’re considering a purchase. 

Use this intent information to guide your marketing. You might be a toy manufacturer who only targets parents with advertising. But after conducting intent-based analysis on TikTok mentions of your brand, you realize pre-teens and adults represent a significant chunk of your customers. By only marketing to parents, you’re missing out on opportunities.

Four steps of the sentiment analysis process

From data collection to taking action, sentiment analysis is a multi-step process. Return to these steps regularly, so you can identify and address customer feelings before they escalate into problems.

1. Collect customer sentiment data

To identify customer sentiments, you need something to analyze: customer communication. Your sentiment analysis tool will continuously gather the data you’ll need from various sources, but you’ll need to help bring in new data by sending review request emails or interacting with customers on social media. Some sources your tool analyzes include: 

• Customer reviews: Find these on Google, your website, and social media platforms 
• Customer surveys: Send out net promoter scores, which ask a customer to rate your company from 1-10, surveys with multiple choice answers, or get open-ended survey responses 
• Social media comments: See what customers are saying about your brand on Twitter, Facebook, Instagram, LinkedIn, and TikTok
• Emails, live chats, or texts: Pull remarks from customer emails, live chat transcripts, or any texts they’ve sent you

2. Analyze customer sentiment

After you’ve gathered all the data you need, use NLP tools to analyze customer sentiment data and identify the sentiment behind each message. 

You could use open-source tools like Python to build your own in-house machine-learning software. But that’s a time-intensive process and you’d need someone with specific expertise. To make this process as convenient as possible, use ready-made SaaS products like an insights and reports tool that will help you see trends in your customer feedback. 

With this tool, you can review recent product ratings and customer sentiment reporting all in one dashboard. You also don’t need to manually submit and review customer communication. Bazaarvoice automatically reviews customer communication on your site on a weekly basis, so you always have up-to-date customer sentiment information. 

3. Identify customer sentiment trends

Once the data is analyzed, use data visualization tools like Google Charts, Tableau, or Grafana to view trends and patterns in your customer sentiment analysis over time.

Visuals give you a quick snapshot of how customers are feeling about a product or service. You’ll see where customer sentiment might be shifting and be able to spot emerging trends — like if shopper mood changes as soon as you change a recipe or upgrade your software. 

When you know the trends, you can find areas of improvement and make informed decisions about your customer experience. If there’s an issue directly related to your products, send that information to your product manager so they can adjust.

Or if people are annoyed with customer service, let the customer service manager know. 

4. Act on customer sentiment results

If customer feedback is negative, fix the underlying issue and communicate the solution to your customers. We’ll go into more detail on how to respond to various types of customer feedback in the next section.

How to respond to types of customer sentiment

No matter what type of customer feedback you receive, you should have a game plan in place to act on it. When a brand responds to customer sentiment, shoppers pay attention.

Nurture positive feelings and reach out to customers with negative (or neutral) sentiment to smooth things over and win over custom.

Address negative customer feedback

If customers leave negative feedback, take a look at the potential causes. Maybe a line of clothing runs too small or you took too long to respond to a question they had.

After you’ve determined the issue, let the customer know you take their feedback seriously. Apologize for any bad experiences they had and consider offering a discount or a refund.

For example, makeup company Il Makiage offers a discount code after a customer says they were unhappy with a product to try and repair the relationship.

Along with sending discounts or refunds, tell the customer you’ll find a solution to their problem. If they express frustration with the shipping process, work with your shipping company to get better delivery windows. If there’s an issue with product quality, sync with your quality manager to develop a better quality assurance process.

Finally, follow up with the customer. Customers expect a response to negative reviews. So make sure their complaint was either resolved or addressed in a satisfactory way. If you let complaints fall through the cracks, it’s harder to fix that negative sentiment.

Acknowledge positive feedback

It’s easy to get swept up in addressing negative feedback, but it’s also important to nurture customers by responding to positive feedback too. Tell them thank you for sharing their experience, and take extra steps like:

• Giving them a discount code as a way to show thanks
• Sharing their positive feedback on your website, on landing pages, or on social media
• Asking them if they’d be willing to review you on Google or social media 

Make a note to follow up with these customers as well so you can check with them to see if they’re still feeling positive about your brand. 

Figure out what’s causing neutral feedback

If a customer gives you neutral feedback, you have a chance to nurture them and try to win them over. They haven’t had a negative experience yet, but they also haven’t been wowed by your brand. Try to get them over to the positive side by giving them a great customer experience.

Reach out to them and ask if there’s anything you can do to improve, and thank them for sharing their thoughts with you. Let them know you’ll take any feedback and act on it to improve your products.

If they say your software doesn’t have a feature they need, let your developer team know and see if it’s something they can add. The best way to improve products is by leveraging customer feedback.

Again, follow up with the customer to let them know if their feedback was addressed. Give them a free trial or a discount to see if you can improve on their neutral sentiment.

3 brand examples of sentiment analysis success

Customer sentiment analysis is more than just reviewing customer data. It becomes much more powerful and has a long-lasting impact when brands take action on the information they get from that analysis.

Here’s some real-world case studies of businesses that looked at their customer sentiment analysis and fixed products or tweaked their marketing strategies as a result. 

Électro Dépôt identifies products that don’t meet quality standards

Électro Dépôt, a European-based business that sells multimedia and home appliances, has 84 stores in France, Spain, and Belgium. They wanted to identify any products that didn’t meet their minimum quality standards. 

A sentiument analysis of customer feedback helped the brand find those areas of improvement. One example: they sold a brand of speaker that couldn’t be used while charging. After noticing this feedback, the speakers were fixed and are now usable while they charge. More importantly, they were able to achieve a product satisfaction score of 4.1/5.

Électro Dépôt also plans to use customer sentiment to help decide on discontinuing products. The business has a goal to remove any products that receive fewer than three stars from customer reviews, as they want to market quality to their customers. 

Nestlé Canada adjusts product recipe after seeing a dip in sales

You know Nestlé’s name, since it’s the world’s biggest food and beverage company with over 2,000 brands. But even the pros can get it wrong sometimes. In 2018, Nestlé Canada made some tweaks to the recipe of a well-loved tea. Once that tea hit the market, Nestlé started getting negative feedback online, along with a drop in sales.

Their customer experience ran a sentiment analysis of customer reviews, then sent the information from the reviews to their product team. A few months later, the company ended up going back to the original tea recipe.

After making the switch back, the average product rating went up from 1.7 stars to 4 stars. Their director of consumer experience said that the data from these reviews gave a lot of power to the decision to revert to the previous formula.

Consumers do the talking for us. They speak, and we listen and take action.

Lee Beech, Director of Consumer Experience, Nestlé Canada

Nestlé continuously receives customer reviews, so they use the solutions from Insights and Reports to help condense that data. Their customer experience team presents that review data, along with reports from customer service, to Nestlé executives to show them what their customer base is thinking.

Kidkraft offers new product option after getting customer feedback

Kidkraft sells children’s products — like dollhouses, swing sets, and outdoor playhouses. The company is understandably concerned with the way parents perceive their products and safety, so they recently started running a customer sentiment analysis every quarter, which shows recommendations for improving products and marketing.

In one case, customer reviews revealed that customers were building one of the popular playhouse models, but then painting them white. After KidKraft discovered this, they began offering a white version of the playhouse, leading to a significant increase in sales. 

The company also changed the way one product was marketed after a recommendation from Bazaarvoice. Reviews showed that customers loved a storage bin underneath one of their play tables, but Kidkraft’s site didn’t even mention that bin. When Kidkraft’s team found this insight, they tweaked the product page and messaging to highlight that feature.

Drive meaningful change with Bazaarvoice’s Insights and Reports

To analyze your data efficiently, you need the right sentiment analysis tool by your side. Bazaarvoice Insights and Reports gives you a clear view into what your customers are thinking and feeling. 

With our reporting tool, you’ll receive a weekly analysis of your customer reviews to see what they like and don’t like, and get actionable insights on how you can improve your products and marketing. We’ll tell you how to:

• Increase review volume
• Improve a low-performing email
• Refresh a product page with new copy 

The tool will also show you how your products stack up against your competition, using our industry benchmarks that compare your ratings and reviews to your competitors. You can get in touch below to learn more. Or continue reading to learn how to find and use consumer insights.

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The smart marketer’s guide to finding and using customer insights

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As insights technology continues to innovate, retailers have no shortage of analytics to glean from the UGC collected across their e-commerce site. Data from UGC can tell you how consumers feel about different your products and brands, which products are performing best, and which products are in need of improvement.

UGC program insights

From product review coverage to overall customer sentiment, these are the five types of insights retailers should consider regularly monitoring through analysis of their UGC program data. And that’s irregardless of whether you’re a Bazaarvoice client or not.

1. Measure the conversion impact of UGC on your site

Your UGC program data should be able to show you exactly how it’s impacting your bottom line and what types of UGC increases conversion rates. 

You want to be able to identify key UGC drivers on your product detail pages (PDPs). You’ll gain valuable insights when you can monitor how shoppers engage with UGC and identify how it affects sales on your e-commerce platform. For example, across the Bazaarvoice Network, we found a 136% lift in conversion rate when shoppers interact with UGC on best-in-class sites.

By measuring shoppers engagement with different UGC, you’ll discover the difference between:

• How customers convert when there’s no content
• When there’s content but they didn’t actively engage with content
• If they passively or actively engaged with content

If you’re a Bazaarvoice client, you can keep close tabs on this UGC program data with the Conversion Impact Report. This report allows you to easily monitor how shoppers engage with UGC and identify how that affects sales on your e-commerce platform.

2. Evaluate product coverage

Evaluate product coverage to see where you can improve your PDPs by seeing which pages lack adequate review coverage. By regularly identifying products with high page views but low review volume, you can better understand popularity and gaps.

You want the content that performs well — which UGC does — to be in as many places as possible. Especially where you know you customers will see it. 

Bazaarvoice clients can easily track the percentage of products or page views with at least one review, regardless of whether the review came in via native, sampling, syndication, or product families, and view a list of popular products with few reviews or a list of products with high page views and low review volume.

3. Track at the brand/product/category level

As a retailer, it’s important for you to know what brands are performing well and which are underperforming. By tracking UGC program insights at the brand, product, and category levels, you can quickly identify areas of opportunity and prioritize accordingly. 

Bazaarvoice clients have access to Product Performance Insights, which allows you to easily view products, brands, or categories that have a low average rating. And with a vendor scorecard, you can take those insights and score brands or products for performance with average rating, review volume, and percent of questions answered.

You can then send the scorecard to your brands to show them what they need to do to improve sales. 

4. Understand customer sentiment

Discover how shoppers really feel about your products to improve your products and/or messaging by leveraging UGC program data to identify areas of praise and complaint. An ideal customer sentiment insights strategy should analyze the lowlights and highlights, revealing what customers love and hate about products.

It should also have the capabilities to extract notable quotes in reviews that will level up your marketing strategy. Nestle Canada noticed a stream of negative customer reviews about a product. Noticing the insights provided, the team changed the recipe and the average rating increased from 1.7 to 4 stars. 

With Bazaarvoice, clients can easily access customer sentiment insights to identify Products with Lowlights/Highlights and Products with Notable Marketing Quotes. This helps you quickly identify what your customers are saying about the products you sell — especially beneficial for your private label items.

5. Track performance

On top of analyzing insights at a micro-level, make sure you’re monitoring insights from a birds eye view. Keep tabs on content collection and overall UGC program performance. A successful UGC program should have insights that allows retailers to monitor broad trends and review volume holistically. 

Retailers should also monitor the performance of post-interaction review request emails asking for reviews and track unexpected changes. For example, you should know quickly if volume drops drastically month-over-month, so you could adjust your strategy accordingly. 

Bazaarvoice clients can seamlessly monitor the performance of their review request emails with the Email Engagement Dashboard, as well as broad trends and review volume with the Program Overview and Inbound Syndication Dashboard.

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By monitoring these five insights, you can easily keep your finger on the pulse of the health of your business and see how your UGC program is helping your business grow. 

If you’re a Bazaarvoice client, jump down to our UGC audit masterclass to take your UGC program to the next level. If you’re not a Bazaarvoice client, you can learn more about our Insights and Reports tools here.

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But actually, they’re arguably the most valuable tools we offer brands and retailers. We’re here to show you why.

Consumers actively seek out advice to guide their decision-making for purchases great and small but aren’t necessarily keen to trust a brand’s own content or advertising. What they do trust, however, are other consumers’ opinions. Bazaarvoice ROI benchmark data shows that when consumers engage with any user-generated content (UGC), like written reviews or customer imagery, there’s a 144% increase in conversion.

A brand can say they’re “authentic,” but only true mutual engagement tells that story. UGC delivers unfiltered insights directly from the source. Asking your customers for their feedback and input is critical, but listening to what they’re proactively telling you will offer a reality check of what’s working and what isn’t.

But what to do with all of that raw data? We have three use cases that illustrate how Bazaarvoice’s Insights and Reports tools can help product marketing managers train their focus on the most effective UGC metrics for tracking their brand’s e-commerce performance.

Fine-tune your customer feedback loops

The product detail page is a make-or-break moment for an e-commerce brand. In our downloadable guide to using customer insights (2021), we point out that shoppers said reviews are what’s most important to them on a product page, followed by product descriptions and imagery. In fact, 92% of consumers said they think online reviews are just as trustworthy as personal recommendations from friends. Reviews reinforce the idea of social proof — since many people chose to take a certain action, it must be the correct one.

For product marketing managers, the number of reviews for each product and determining which products need more review coverage will be top-of-mind considerations for analyzing how each product detail page is performing. Bazaarvoice analyzes data for clients (using machine learning in the case of Premium Network Insights) then surfaces that information for the client to take action on.

This helps marketers pinpoint pages with too few reviews or uncover opportunities to deliver more value by integrating visual UGC sourced from social media.

For insulated water bottle brand Takeya, using Bazaarvoice’s AI tools led them to display reviews for all of their products on both their own and partner sites, like Target.com, leading to a measurable increase in sales via both platforms. The brand used both Insights and Reports and Galleries analytics to achieve different ends.

Direct-to-consumer cycling gear brand Le Col uses reviews not only to build social proof of their brand’s quality but also to help customers find the right product fit. Andrew Longley, Le Col’s head of digital, notes that “post-purchase sizing was one of our biggest reasons for seeing customer returns. Our review implementation has helped us get on top of this issue.”

Marketers and UX designers can consider that each element on the product detail page is its own customer touchpoint, from the product description and the gallery of images to the social proof of ratings and reviews. And each of these touchpoints offers the potential for mining rich insights that can help improve the user experience and increase conversion.

Share social media content that converts

Reviews are a powerful form of customer feedback, but social media provides a window into how a brand actually fits into our day-to-day lives. Seeing how an outfit looks on a customer who actually bought it or how a sofa fits in someone’s living room lets the customer relate to the product and picture it in their own world.

The availability of photos from previous shoppers — not influencers — is important when making online purchasing decisions. Fashion shoppers, in particular, seek guidance in their decision-making to close the gap between online and offline shopping and the ability to compare and try on items firsthand.

In the past two years, e-commerce fashion brands and marketplaces have experienced explosive growth. According to a Shopify fashion industry roundup, “in the US alone, the apparel and accessory industries accounted for 29.5% of all ecommerce sales in 2021. In Europe, it’s expected that by 2025, each consumer will spend $999 on fashion-related items over the course of a year.”

With shoppers six times more likely to purchase an item if the product page contains images from social media, unquestionably, we’re just beginning to crest the wave of social commerce — the ability to make purchases directly from social media.

American clothing brand Tuckernuck uses Bazaarvoice’s gallery modules across a number of high-traffic pages to integrate social UGC directly into their website, including a full-page #tuckernucking gallery to highlight Instagram tagged content. As a result, the brand has seen a 140% lift in conversion rate, a 164% increase in time on site, and a higher average order value.

Pro tip: Tuckernuck also leverages Bazaarvoice’s social analytics tools (which aren’t part of Insights and Reports but actually are part of our Social Commerce portal) to zoom in on the details of how their social content is driving conversions. These tools combine computer vision and AI to learn which content is engaging their customers and when to post it, and the value of each impression in terms of revenue.

Tuckernuck is able to clearly identify which content drives the most traffic from social to the site and get real-time metrics on product interactions to help continually optimize their content strategy.

In the future, marketers will see the increased prominence of behavioral analytics, opening up whole new pathways for understanding customers. Today, the rise of AI-powered analytics tools is leading to more curated and tailored user experiences based on taste, preference, usage patterns, and personality traits.

Convert negative feedback into positive action

Sentiment analysis reveals insights into customers’ feelings, opinions, and attitudes toward a brand. It’s generally measured on a spectrum between positive and negative, but as analysis tools grow in sophistication, they can also extract emotional states like anger, frustration, or joy. 

Additionally, sentiment analysis can indicate customer purchase intent and level of interest. Marketers can decide to tune into holistic brand analysis or focus on specific products and services to help identify trends and surface insights, such as:

• Whether your brand reputation corresponds with a particular customer sentiment?
• How customers feel about your products, based on reviews
• If customer sentiment has shifted recently for products

Within a month of hitting the shelves, customers reacted strongly to Nestlé Canada’s newly formulated recipe for one of their iced tea products. Negative reviews and customer complaints poured through the brand’s feedback channels, and in a short amount of time, sales dropped.

Customer reviews and questions provide the Nestlé team with an in-depth look into the minds of their customers, directly informing brand strategy.

We surface opportunities in our coverage area and our consumer sentiment analysis provides direct feedback on what your clients love about your products or where you may need to make tweaks.

Amy Degner, Product Marketing Manager, Bazaarvoice

AI and natural language processing (NLP) algorithms are continuously evolving to develop a nuanced vocabulary that expresses a range of emotions and perspectives. Bazaarvoice platforms can help to render this analysis in real time, because we refresh sentiment weekly. Meaning, it’s easier to align every department on customer behavior and pain points and to respond with both short- and long-term actions to retain customers.

Leverage Bazaarvoice Insights and Reports tools for a competitive edge

Product marketers, or really anyone managing a product, know they need to triangulate customer data from multiple sources for the most effective e-commerce reporting. Bazaarvoice’s Insights and Reports tools roll all of that rich information into a single, customizable dashboard view that helps you track performance and refine tactics at scale.

Looking at the volume and distribution of reviews helps to even out marketing program efforts and refine the most effective approaches for collecting and harnessing customer feedback. Gathering UGC from social media generates real-life social proof that helps brands communicate authentically and tell compelling and resonant brand stories. Addressing negative sentiments head-on not only advances product and experience development but also builds customer trust and loyalty through transparency.

Bazaarvoice’s Insights and Reports arm you with the data that prepares your team for growth and shows your retail partners that you’re putting in the work it takes to turn browsers into buyers.

Learn more here. Or request a free demo below.

An emotion can change multiple times throughout a day, but sentiment is the overarching attitude of a consumer towards a brand. It’s the difference between customer loyalty or no loyalty. Return business or no business. So unless you plan on losing your customers, it’s time you started on your customer sentiment strategy.

Chapters:

1. What is customer sentiment?
2. Why is customer sentiment important?
3. How to collect and measure customer sentiment
4. Using reports to improve customer sentiment analysis
5. Gain insights from authentic customer sentiment

What is customer sentiment?

Customer sentiment is a metric that indicates customers’ feelings, opinions, and attitudes towards a brand. It can be measured on a scale between positive and negative, or it can be measured as an emotion — like angry, frustrated, or happy. Customer sentiment can also describe a customer’s urgency and intent in terms like interested.

Feedback can be both qualitative and quantitative, and it can address your entire brand or a specific product or service.

Imagine two shoppers walking through a grocery store deciding which box of cereal to buy. They probably think they’re logical shoppers: they compare price, size, nutrition, and flavor to make sure they each get the best option. Most people believe they shop logically, but in reality, most of the time, buyers are swayed by emotion, not logic. They base decisions on how each cereal makes them feel. There’s a reason Tony the Tiger is still going strong.

The way customers feel about a product, service, or brand can make or break e-commerce companies. That’s why measuring customer sentiment is mission-critical. Finding recurring themes in customer sentiment will give you a better picture of the positive and negative aspects of your business or product, so you can make improvements.

Why is customer sentiment important?

Customer sentiment provides feedback about a brand’s most important task: Eliciting a positive, eager response from buyers. No matter the sentiment, you should know that positive sentiment means Keep it up! And negative sentiment means Something’s not working.

Keeping your finger on the pulse of customer sentiment can help you better shape your customers’ buying experiences, making their path to purchase as frictionless as possible. It also means you can tweak product and service offerings to better meet your customers’ needs and increase retention.

Measuring customer sentiment helps companies identify trends and answer broad questions like:

• Do certain teams, products, or days of the week usually correspond with a particular customer sentiment?
• How do customers feel about your latest marketing campaign?
• Has customer sentiment shifted recently?

If customer sentiment is negative, you risk losing your customers altogether. According to Zendesk’s Customer Experience Trends Report 2021, half of your customers will defect to a competitor after they have just one bad experience with you. If they have multiple bad experiences, that number snowballs to 80%.

Tactics to collect and measure customer sentiment

There’s several tactics you can use, from surveys and social media to on-page feedback, one-on-one interviews, and AI-powered analysis.

Solicit reviews and conduct satisfaction surveys

Reviews and satisfaction surveys contain valuable qualitative and quantitative data on existing customers about what they loved about your brand and product. But also what they didn’t. Spend a few minutes (or hours, if you have a large number of reviews) each week looking through reviews on Google, Yelp, Facebook, and Trustpilot to understand your customer sentiment.

You should definitely request a review or a one-to-five-question survey after a customer has bought your product or service. With a survey, only you see the answer (unless you publish it), while reviews are public. Longer, more in-depth surveys should be sent just once per quarter — to your most active customers — to drive customer loyalty.

When collecting reviews, aim to collect a high volume of recent reviews. You can do this by using a targeted approach, like a post-interaction email (PIE). A PIE message, like this one from Amazon, prompts customers to give a star rating after making a purchase.

This text review invite from Sura Eats (another targeted approach) makes it easy for customers to provide feedback by tapping a URL.

If you don’t have many reviews, conduct a customer satisfaction survey to collect sentiment information from your existing customers. Justworks uses an always-on survey method by inviting users to rate them on the dashboard’s main page.

Metrics to measure sentiment via reviews include:

• Average rating: Let us know how we did! On a scale of one to five stars.
• Net Promoter Score (NPS): How likely are you to recommend this product (or brand) to a friend or colleague? On a scale of 0 to 10.
• Would recommend: Would you buy this product again? Or Would you recommend this to a friend? Yes or no.

If you want to dig deeper into customer sentiment patterns, you should search within unstructured feedback (like comment boxes) for common adjectives that indicate positive or negative sentiment. For example, great, awful, easy, difficult, etc. Then you can organize those responses by sentiment and answer questions like, What feature makes customers the happiest? or What part of the service creates the most tension?

Engage with customers on social media

McDonald’s famously used social media conversations to make a decision to serve breakfast all day in 2015. A dream come true. Today, it’s a basic expectation that companies actively engage with customers on social media. Doing this allows you to collect feedback from a wider, more candid audience than reviews and surveys, which are typically only sent to current or recent customers. Anyone who has an opinion about your brand can mention you or comment on your page.

A flood of complaints on Twitter can help you address product problems. Or, at least unearth an opportunity to be cheeky (if that’s your style) like Wendy’s does on Instagram.

On the other hand, a pattern of praise on Facebook — like this one about The Kindler Hotel — can clue you in to what makes your customers happy.

To measure customer sentiment via social media, monitor the platforms where most of your customers are, along with hashtags and keywords related to your brand. Globally, Facebook, YouTube, FB Messenger, and Instagram command the largest consumer audiences.

Respond to comments, mentions, and DMs. Post polls or ask questions to solicit feedback like Stitch Fix does on Instagram.

Then pay attention to how often and why customers engage with you. Access your business profile’s analytics or insights feature to track audience feedback. To monitor customer sentiment at scale, you can create a Mentions stream in Hootsuite for each of your active social accounts or use Sprout Social’s Social Listening tool.

Conduct user interviews

User interviews — in-depth, one-on-one conversations with existing and potential buyers — help you understand what motivates individual customers and gain new ideas for products and communication strategies.

“…even a few conventional one-on-one interviews yield essentially the same data as several focus groups. Additionally, there is now a lot of evidence that personal interviews yield deep insights that can’t be obtained from focus groups.”

Gerald Zaltman, Harvard Business School professor

Inviting customers to give feedback via a direct interview is messier and more time-consuming than conducting a satisfaction survey. But user interviews can yield powerful insights into how your customers feel about your brand. According to Zaltman, you should conduct customer interviews when:

1. Introducing a new product
2. Extending the value of a current product
3. You want to reframe your existing brand

To collect interview participants, reach out to specific reviewers or commenters on social media. Some companies hire a professional recruiting firm to help them conduct research. You can also advertise for participants via a social media, Craigslist, or Reddit post (like the one below) and then screen them using a survey like Google Forms or SurveyMonkey.

Measuring feedback from interviews is more complicated than survey or review feedback because the responses are unstructured. Narrow down the feedback by starting with a clear goal, like Find out why half of shoppers are abandoning items in their cart.

Next, craft a discussion guide using clinical psychology and sociology techniques, like metaphor-elicitation. With this technique, you ask consumers to think about a topic (e.g., your brand) and select images that convey their feelings about it. Probe deeper by asking why each image is relevant to them. Then look for patterns in the responses and categorize them by theme, so you can draw conclusions from those themes.

Monitor customer service calls and live chat

Every opportunity to measure customer sentiment isn’t necessarily planned — or even online. When customers contact your call center to ask questions or complain about negative experiences, you should have a way to measure and track their feelings about your brand.

This tactic is similar to engaging with customers via social media. Both are organic, but with phone calls and live chat, your customer support reps have to be more intentional about collecting and organizing customer feedback.

For example, let’s say a customer calls your support number to complain about a missing tracking number. You can measure their sentiment by having a customer service rep manually summarize the interaction and make anecdotal notes in your CRM. This summary of the Voice of the Customer (VoC) can be put in a structured form or an unstructured comment box.

Use AI to conduct sentiment analysis

Sentiment analysis is a process that uses conversational AI, machine learning, and natural language processing (NLP) to analyze text data and detect sentiment patterns in an instant. Centralized sentiment analysis tools cast a wider net than other types of feedback. They can analyze sentiment across social media as well as review sites, forums, blogs, news articles, etc.

Sentiment analysis tools allow practitioners to classify sentences as positive, neutral, or negative. Komprehend demonstrates this in their demo by classifying the sentence The fit was spot on as a positive sentiment.

AI models can recognize that spot on carries a similar sentiment as great or accurate. The algorithms are able to do this because they turn words into vectors, then demonstrate the relationship between those words as distances between points.

During his TEDx talk, Andy Kim illustrates the way this works. In his example, the words Lion and Cat are grouped together on a coordinate plane, and Ford and Honda are grouped together.

Sentiment analysis tools can detect emotions like happiness or frustration using keywords, their variations, and Contextual Semantic Search (CSS). They can also detect that phrases and synonyms like superior or very durable express good quality, and they use aspect-based classifiers to identify critical product issues like the phone’s battery life is too short.

You can buy a pre-built SaaS sentiment analysis tool, or you can try to build your own open-source solution (if you have an excellent team of data scientists and engineers).

Using reports to improve customer sentiment analysis

Measuring customer data isn’t useful unless it can be analyzed and used to drive operational and marketing strategies. Tracking customer sentiment is no different. There’s simply too much data to process manually. That’s why it’s important to convert customer sentiment data into digestible reports.

First, pool all your customer sentiment data into one spot, so you can analyze it in a low-effort, cost-effective way. Then segment your customer feedback with tags like location, product, and sentiment. Look for patterns and correlations in these tags and convert them into reports.

This can help you figure out if customers like your pricing and packaging, what delights them the most about a certain product, and what’s causing the most problems.

Use your reports to create a visual chart or dashboard that shows overall sentiment, sentiment over time, and sentiment filtered by rating or by topic. For example, compare a selected date range to 12 months ago to get a year-over-year comparison of customer sentiment.

Cleaning technology company Kärcher was able to collect thousands of reviews about a new product across many different retails sites and analyze them in one central dashboard. By doing this, they learned that many customers misunderstand the purpose of the product and even how to use it. Kärcher was able to then use this information to adjust messaging and clarify the product’s intent.

Gain insights from authentic customer sentiment

Now that you know how to measure customer sentiment using reviews, social media feedback, and organic interactions, next you need to learn how to perform a customer sentiment analysis to learn how to retain loyal customers and win over unhappy shoppers.

Or you can check out Bazaarvoice Insights and Reports tools, which use AI and machine learning to analyze customer sentiment for you.

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The smart marketer’s guide to finding and using customer insights

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It’s a huge pain point for brands to assess how they’re performing at retail. Join us as we walk through Bazaarvoice’s new Premium Network Insights to uncover how your consumer sentiment and UGC performance stack up to your named competitors at retail.

We’ll show you how to use this dashboard to easily prepare for important meetings, improve products and messaging, prioritize resources, and feel confident adopting strategies that make the biggest impact on your bottom line.

To learn more, check out our e-book on finding and using customer insights